Biology subjects

Hyman, J. M.

Publications and source records attributed to Hyman, J. M..

4 recordsLinked to original sources

bioRxiv2026

Contact heterogeneity in network reconstruction: evaluating synthetic graph generators for rabies transmission modeling

Canine rabies remains endemic across much of Africa and Asia, causing approximately 59,000 global deaths yearly. Network-based models that reconstruct contact patterns are central to predicting canine rabies transmission and to designing targeted interventions, but the choice of network generator algorithm may alter network structure and outbreak predictions. We therefore compared five network generators using five empirical free-roaming dog contact networks from Chad, Indonesia, and Guatemala in network topology and outbreak dynamics under susceptible-exposed-infectious-removed (SEIR) and susceptible-infectious-susceptible (SIS) transmission across a range of infectious-period lengths. We found that the degree-corrected stochastic block model (DCSBM) most consistently reproduces both empirical topology and outbreak dynamics, achieving a Mahalanobis distance of 17.6 to the empirical Chad network compared to a range of 21.0 to 220.7 across the other generators and yielding similar results for the other four locations. Moreover, the SIS and SEIR outbreak dynamics reveal that the DCSBM overall best reproduces the simulated empirical outbreak dynamics, but that the consequences of generator choice are not uniform: network reconstruction matters most precisely for short-infectious-window pathogens like canine rabies and progressively less for long-infectious-window pathogens. By an infectious window of nine days, the outbreak dynamics across four of five generators converge to within two percentage points of the empirical reference, even when their underlying topologies differ substantially. This pathogen-dependent sensitivity offers a principled criterion for when generator selection is consequential and when it is not. This work reiterates the importance of both degree heterogeneity and community structure for faithful contact-network reconstruction in canine rabies modeling, particularly for short-window infectious diseases where contact heterogeneity drives transmission. For longer-window pathogens, simpler generators are likely adequate.

ecology↗

bioRxiv2021

A Process-Based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Mosquito Density

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens and Culex restuans mosquito populations. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like the West Nile virus.

ecology↗

bioRxiv2019

Quantification of human-mosquito contact rate using surveys and its application in determining dengue viral transmission risk

Aedes-borne viral diseases, including dengue fever, chikungunya, and Zika, have been surging in incidence and spreading to new areas where their mosquito vectors thrive. To estimate viral transmission risks, availability of accurate local transmission parameters is essential. One of the most important parameters to determine infection risk is the human-mosquito contact rate. However, this rate has rarely been characterized due to the lack of a feasible research method. In this study, human-mosquito contact rates were evaluated in two study sites within the Greater New Orleans Region by asking a group of survey participants to estimate mosquito bites they experienced in the past 24 hours. The fraction of the mosquito bites attributed to Ae. aegypti or Ae. albopictus was estimated by human landing sampling. The results showed a significantly higher outdoor mosquito bite exposure than indoor exposure. The number of reported mosquito bites was positively correlated with the time that study participants spent outside during at-risk periods. There was also a significant effect of the study site on outdoor bite exposure, possibly because of the difference in the numbers of host-seeking mosquitoes. We use a mathematical dengue virus transmission model to estimate the transmission risks in the study areas based on local conditions. This compartmental model demonstrated how the observed difference in the human-Aedes contact rates in the two study sites would result in differential dengue transmission risks. This study highlights the practicality of using a survey to estimate human-mosquito contact rates and serves as a basis for future evaluations. Combined with the use of mathematical modeling, this innovative method may lead to more effective mosquito-borne pathogen prevention and control.\n\nAuthor summaryEven though the human-mosquito contact rate is among the most important indicators of mosquito-borne viral transmission risk, it is rarely characterized in the field. Human Landing Capture is a gold standard method to quantify this rate, but it ignores variables such as human behaviors and lifestyles. In this study, we tested the feasibility of using surveys to quantify mosquito bite exposure in the Southern United States. The survey results, combined with mosquito species proportion data, were used to estimate the contact rate. These rates are key parameters used in mathematical models to determine transmission risks. We found that bite exposure occurred more often outside homes and people who spent more time outdoors in the evening and night had a higher exposure. Our model analysis shows that the human-mosquito contact rate is one of the most important parameters determining outbreak potential. Disease control programs should focus their efforts on reducing this rate in addition to the mosquito density. Future studies should test if the entomological contact rates described by surveys correlate with disease incidences or other entomological indices. This study highlights the importance of characterizing how vector-human contact rates may respond to changing human behaviors and environments.

ecology↗

bioRxiv2017

Using an agent-based sexual-network model to guide mitigation efforts for controlling chlamydia

We create and analyze a stochastic heterosexual agent-based bipartite network model to help understand the spread of chlamydia trachomatis. Chlamydia is the most common sexually transmitted infection in the United States and is major cause of infertility, pelvic inflammatory disease, and ectopic pregnancy among women. We use an agent-based network model to capture the complex heterogeneous assortative sexual mixing network of men and women. Both long-term and casual partnerships are modeled with different sexual contact frequencies and condom use. We use simulations to compare the effectiveness of intervention strategies based on randomly screening people for infection, treating the partners of infected people, and rescreening for infection after treatment. We compare the difference between treating the partners of an infected person both with, and without, testing them first for infection. The highest prevalence is among young sexually active individuals. We calibrate the model parameters to agree with recent survey data showing chlamydia prevalence of 14% of the women and 9% of the men in the 15 - 25 year-old African American residents of New Orleans, Louisiana. We observed that although increased chlamydia screening and treating most of the partners of infected people will reduce the incidence, these mitigations alone are not sufficient to control the epidemic. The model predicts that the current epidemic can brought under control once over half of the partners of infected people are tested and treated.

epidemiology↗